Virality detection and control strategies in rumor models

TL;DR

This paper analyzes rumor spread dynamics using a modified Maki-Thompson model, distinguishing organic growth from artificial injection, and proposes intervention strategies to control rumor lifespan based on network placement.

Contribution

It introduces a method to differentiate natural versus artificial rumor virality through autocorrelation patterns and validates control strategies for rumor lifespan manipulation.

Findings

Oscillatory autocorrelation indicates organic growth in rumor spread.

The model can identify the origin of rumor virality in social networks.

Targeted interventions can effectively extend or shorten rumor lifespan.

Abstract

We study the dynamics and intervention strategies of a rumor using the modified Maki-Thompson model. A key challenge in social networks is distinguishing between natural increases in transmissibility and artificial injections of rumor spreaders, such as through broadcast events or astroturfing. Using stochastic simulations, we compare two scenarios: one with organic growth in transmissibility and another with externally injected spreaders. Although both lead to high autocorrelation, only the organic growth produces oscillatory patterns in autocorrelation at multiple lags, an effect we can analytically explain using the $N$-intertwined mean-field approximation. This distinction offers a practical tool to identify the origin of rumor virality and also infer its transmissibility. Our approach is validated analytically and tested on real-world data from Twitter during the announcement of the Higgs boson discovery. In addition to detection, we also explore control strategies. We show that the average lifetime of a rumor can be manipulated through targeted interventions: placing spreaders at specific locations in the network. Depending on their placement, these interventions can either extend or shorten the lifespan of the rumor.